# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import torch
import torch.nn as nn
BN_MOMENTUM = 0.1
logger = logging.getLogger(__name__)
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class PoseResNet(nn.Module):
def __init__(self, block, layers, cfg, global_mode, **kwargs):
self.inplanes = 64
extra = cfg.POSE_RES_MODEL.EXTRA
self.extra = extra
self.deconv_with_bias = extra.DECONV_WITH_BIAS
super(PoseResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.global_mode = global_mode
if self.global_mode:
self.avgpool = nn.AvgPool2d(7, stride=1)
self.deconv_layers = None
else:
# used for deconv layers
self.deconv_layers = self._make_deconv_layer(
extra.NUM_DECONV_LAYERS,
extra.NUM_DECONV_FILTERS,
extra.NUM_DECONV_KERNELS,
)
# self.final_layer = nn.Conv2d(
# in_channels=extra.NUM_DECONV_FILTERS[-1],
# out_channels=17,
# kernel_size=extra.FINAL_CONV_KERNEL,
# stride=1,
# padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0
# )
self.final_layer = None
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False
),
nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _get_deconv_cfg(self, deconv_kernel, index):
if deconv_kernel == 4:
padding = 1
output_padding = 0
elif deconv_kernel == 3:
padding = 1
output_padding = 1
elif deconv_kernel == 2:
padding = 0
output_padding = 0
return deconv_kernel, padding, output_padding
def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
assert num_layers == len(num_filters), \
'ERROR: num_deconv_layers is different len(num_deconv_filters)'
assert num_layers == len(num_kernels), \
'ERROR: num_deconv_layers is different len(num_deconv_filters)'
layers = []
for i in range(num_layers):
kernel, padding, output_padding = \
self._get_deconv_cfg(num_kernels[i], i)
planes = num_filters[i]
layers.append(
nn.ConvTranspose2d(
in_channels=self.inplanes,
out_channels=planes,
kernel_size=kernel,
stride=2,
padding=padding,
output_padding=output_padding,
bias=self.deconv_with_bias
)
)
layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
layers.append(nn.ReLU(inplace=True))
self.inplanes = planes
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
# x = self.deconv_layers(x)
# x = self.final_layer(x)
if self.global_mode:
g_feat = self.avgpool(x)
g_feat = g_feat.view(g_feat.size(0), -1)
s_feat_list = [g_feat]
else:
g_feat = None
if self.extra.NUM_DECONV_LAYERS == 3:
deconv_blocks = [
self.deconv_layers[0:3], self.deconv_layers[3:6], self.deconv_layers[6:9]
]
s_feat_list = []
s_feat = x
for i in range(self.extra.NUM_DECONV_LAYERS):
s_feat = deconv_blocks[i](s_feat)
s_feat_list.append(s_feat)
return s_feat_list, g_feat
def init_weights(self, pretrained=''):
if os.path.isfile(pretrained):
# logger.info('=> init deconv weights from normal distribution')
if self.deconv_layers is not None:
for name, m in self.deconv_layers.named_modules():
if isinstance(m, nn.ConvTranspose2d):
# logger.info('=> init {}.weight as normal(0, 0.001)'.format(name))
# logger.info('=> init {}.bias as 0'.format(name))
nn.init.normal_(m.weight, std=0.001)
if self.deconv_with_bias:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
# logger.info('=> init {}.weight as 1'.format(name))
# logger.info('=> init {}.bias as 0'.format(name))
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
if self.final_layer is not None:
logger.info('=> init final conv weights from normal distribution')
for m in self.final_layer.modules():
if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
logger.info('=> init {}.weight as normal(0, 0.001)'.format(name))
logger.info('=> init {}.bias as 0'.format(name))
nn.init.normal_(m.weight, std=0.001)
nn.init.constant_(m.bias, 0)
pretrained_state_dict = torch.load(pretrained)
logger.info('=> loading pretrained model {}'.format(pretrained))
self.load_state_dict(pretrained_state_dict, strict=False)
elif pretrained:
logger.error('=> please download pre-trained models first!')
raise ValueError('{} is not exist!'.format(pretrained))
else:
logger.info('=> init weights from normal distribution')
for m in self.modules():
if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.normal_(m.weight, std=0.001)
# nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.ConvTranspose2d):
nn.init.normal_(m.weight, std=0.001)
if self.deconv_with_bias:
nn.init.constant_(m.bias, 0)
resnet_spec = {
18: (BasicBlock, [2, 2, 2, 2]),
34: (BasicBlock, [3, 4, 6, 3]),
50: (Bottleneck, [3, 4, 6, 3]),
101: (Bottleneck, [3, 4, 23, 3]),
152: (Bottleneck, [3, 8, 36, 3])
}
def get_resnet_encoder(cfg, init_weight=True, global_mode=False, **kwargs):
num_layers = cfg.POSE_RES_MODEL.EXTRA.NUM_LAYERS
block_class, layers = resnet_spec[num_layers]
model = PoseResNet(block_class, layers, cfg, global_mode, **kwargs)
if init_weight:
if num_layers == 50:
if cfg.POSE_RES_MODEL.PRETR_SET in ['imagenet']:
model.init_weights(cfg.POSE_RES_MODEL.PRETRAINED_IM)
logger.info('loaded ResNet imagenet pretrained model')
elif cfg.POSE_RES_MODEL.PRETR_SET in ['coco']:
model.init_weights(cfg.POSE_RES_MODEL.PRETRAINED_COCO)
logger.info('loaded ResNet coco pretrained model')
else:
raise NotImplementedError
return model